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Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization

  • Garvit Agarwal
    Garvit Agarwal
    Joint Center for Energy Storage Research (JCESR), Lemont, Illinois 60439, United States
    Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
  • Hieu A. Doan
    Hieu A. Doan
    Joint Center for Energy Storage Research (JCESR), Lemont, Illinois 60439, United States
    Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
    More by Hieu A. Doan
  • Lily A. Robertson
    Lily A. Robertson
    Joint Center for Energy Storage Research (JCESR), Lemont, Illinois 60439, United States
    Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
  • Lu Zhang
    Lu Zhang
    Joint Center for Energy Storage Research (JCESR), Lemont, Illinois 60439, United States
    Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
    More by Lu Zhang
  • , and 
  • Rajeev S. Assary*
    Rajeev S. Assary
    Joint Center for Energy Storage Research (JCESR), Lemont, Illinois 60439, United States
    Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
    *Email: [email protected]. Phone: 630-252-3536.
Cite this: Chem. Mater. 2021, 33, 20, 8133–8144
Publication Date (Web):October 14, 2021
https://doi.org/10.1021/acs.chemmater.1c02040
Copyright © 2021 UChicago Argonne, LLC, Operator of Argonne National Laboratory. Published by American Chemical Society

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    Abstract

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    Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redox-active materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the-art machine learning (ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. We anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.chemmater.1c02040.

    • PCA, analysis of 100 MBO runs on the 1400 BzNSN data set, SMILES representation and DFT-computed properties of the 1400 BzNSN data set, and SMILES representation and DFT-computed properties of the 100 MBO-suggested molecules from the 1 million BzNSN dataset (PDF)

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    Cited By

    This article is cited by 12 publications.

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    2. Akash Jain, Ilya A. Shkrob, Hieu A. Doan, Keir Adams, Jeffrey S. Moore, Rajeev S. Assary. Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries. ACS Applied Materials & Interfaces 2023, 15 (50) , 58309-58319. https://doi.org/10.1021/acsami.3c11741
    3. Konstantin Karandashev, Jan Weinreich, Stefan Heinen, Daniel Jose Arismendi Arrieta, Guido Falk von Rudorff, Kersti Hermansson, O. Anatole von Lilienfeld. Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design. Journal of Chemical Theory and Computation 2023, 19 (23) , 8861-8870. https://doi.org/10.1021/acs.jctc.3c00822
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